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## What is the mean of log transformed data?

Log transformation is a data transformation **method in which it replaces each variable x with a log(x)**. … In other words, the log transformation reduces or removes the skewness of our original data. The important caveat here is that the original data has to follow or approximately follow a log-normal distribution.

## How do you find the standard deviation of a log transformed data?

To find a standard deviation, we calculate the differences between each observation and the mean, square and add. On the log scale, **we take the difference between each log transformed observation and subtract the log geometric mean**. The antilog of the standard deviation is not measured in mmol/litre.

## What does log of a variable mean?

A regression model will have unit changes between the x and y variables, where a single unit change in x will coincide with a constant change in y. Taking the log of one or both variables will effectively change the case from a unit change to a percent change. … **A logarithm is the base of a positive number**.

## What does taking log of data do?

There are two main reasons to use logarithmic scales in charts and graphs. The first is to respond to skewness towards large values; i.e., cases in which one or a few points are much larger than the bulk of the data. The second is **to show percent change or multiplicative factors**.

## What variables can be transformed to achieve linearity?

Methods of Transforming Variables to Achieve Linearity

Method | Transform | Regression equation |
---|---|---|

Quadratic model | DV = sqrt(y) | sqrt(y) = b_{} + b_{1}x |

Reciprocal model | DV = 1/y | 1/y = b_{} + b_{1}x |

Logarithmic model | IV = log(x) | y= b_{} + b_{1}log(x) |

Power model | DV = log(y) IV = log(x) | log(y)= b_{} + b_{1}log(x) |

## What does adjusted R 2 mean?

Adjusted R-squared is **a modified version of R-squared that has been adjusted for the number of predictors in the model**. The adjusted R-squared increases when the new term improves the model more than would be expected by chance. It decreases when a predictor improves the model by less than expected.

## How does a linear transformation affect the mean and standard deviation of a random variable?

Linear Transformations**Adding the same number a (which could be negative) to each value of a random variable**: Adds a to measures of center and location (mean, median, quartiles, percentiles). Does not change measures of spread (range, IQR, standard deviation).